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PaddleRS/paddlers/models/ppdet/data/source/keypoint_coco.py

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
this code is base on https://github.com/open-mmlab/mmpose
"""
import os
import cv2
import numpy as np
import json
import copy
import pycocotools
from pycocotools.coco import COCO
from .dataset import DetDataset
from paddlers.models.ppdet.core.workspace import register, serializable
@serializable
class KeypointBottomUpBaseDataset(DetDataset):
"""Base class for bottom-up datasets.
All datasets should subclass it.
All subclasses should overwrite:
Methods:`_get_imganno`
Args:
dataset_dir (str): Root path to the dataset.
anno_path (str): Relative path to the annotation file.
image_dir (str): Path to a directory where images are held.
Default: None.
num_joints (int): keypoint numbers
transform (composed(operators)): A sequence of data transforms.
shard (list): [rank, worldsize], the distributed env params
test_mode (bool): Store True when building test or
validation dataset. Default: False.
"""
def __init__(self,
dataset_dir,
image_dir,
anno_path,
num_joints,
transform=[],
shard=[0, 1],
test_mode=False):
super().__init__(dataset_dir, image_dir, anno_path)
self.image_info = {}
self.ann_info = {}
self.img_prefix = os.path.join(dataset_dir, image_dir)
self.transform = transform
self.test_mode = test_mode
self.ann_info['num_joints'] = num_joints
self.img_ids = []
def parse_dataset(self):
pass
def __len__(self):
"""Get dataset length."""
return len(self.img_ids)
def _get_imganno(self, idx):
"""Get anno for a single image."""
raise NotImplementedError
def __getitem__(self, idx):
"""Prepare image for training given the index."""
records = copy.deepcopy(self._get_imganno(idx))
records['image'] = cv2.imread(records['image_file'])
records['image'] = cv2.cvtColor(records['image'], cv2.COLOR_BGR2RGB)
records['mask'] = (records['mask'] + 0).astype('uint8')
records = self.transform(records)
return records
def parse_dataset(self):
return
@register
@serializable
class KeypointBottomUpCocoDataset(KeypointBottomUpBaseDataset):
"""COCO dataset for bottom-up pose estimation.
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
COCO keypoint indexes::
0: 'nose',
1: 'left_eye',
2: 'right_eye',
3: 'left_ear',
4: 'right_ear',
5: 'left_shoulder',
6: 'right_shoulder',
7: 'left_elbow',
8: 'right_elbow',
9: 'left_wrist',
10: 'right_wrist',
11: 'left_hip',
12: 'right_hip',
13: 'left_knee',
14: 'right_knee',
15: 'left_ankle',
16: 'right_ankle'
Args:
dataset_dir (str): Root path to the dataset.
anno_path (str): Relative path to the annotation file.
image_dir (str): Path to a directory where images are held.
Default: None.
num_joints (int): keypoint numbers
transform (composed(operators)): A sequence of data transforms.
shard (list): [rank, worldsize], the distributed env params
test_mode (bool): Store True when building test or
validation dataset. Default: False.
"""
def __init__(self,
dataset_dir,
image_dir,
anno_path,
num_joints,
transform=[],
shard=[0, 1],
test_mode=False):
super().__init__(dataset_dir, image_dir, anno_path, num_joints,
transform, shard, test_mode)
self.ann_file = os.path.join(dataset_dir, anno_path)
self.shard = shard
self.test_mode = test_mode
def parse_dataset(self):
self.coco = COCO(self.ann_file)
self.img_ids = self.coco.getImgIds()
if not self.test_mode:
self.img_ids = [
img_id for img_id in self.img_ids
if len(self.coco.getAnnIds(
imgIds=img_id, iscrowd=None)) > 0
]
blocknum = int(len(self.img_ids) / self.shard[1])
self.img_ids = self.img_ids[(blocknum * self.shard[0]):(blocknum * (
self.shard[0] + 1))]
self.num_images = len(self.img_ids)
self.id2name, self.name2id = self._get_mapping_id_name(self.coco.imgs)
self.dataset_name = 'coco'
cat_ids = self.coco.getCatIds()
self.catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)})
print('=> num_images: {}'.format(self.num_images))
@staticmethod
def _get_mapping_id_name(imgs):
"""
Args:
imgs (dict): dict of image info.
Returns:
tuple: Image name & id mapping dicts.
- id2name (dict): Mapping image id to name.
- name2id (dict): Mapping image name to id.
"""
id2name = {}
name2id = {}
for image_id, image in imgs.items():
file_name = image['file_name']
id2name[image_id] = file_name
name2id[file_name] = image_id
return id2name, name2id
def _get_imganno(self, idx):
"""Get anno for a single image.
Args:
idx (int): image idx
Returns:
dict: info for model training
"""
coco = self.coco
img_id = self.img_ids[idx]
ann_ids = coco.getAnnIds(imgIds=img_id)
anno = coco.loadAnns(ann_ids)
mask = self._get_mask(anno, idx)
anno = [
obj for obj in anno
if obj['iscrowd'] == 0 or obj['num_keypoints'] > 0
]
joints, orgsize = self._get_joints(anno, idx)
db_rec = {}
db_rec['im_id'] = img_id
db_rec['image_file'] = os.path.join(self.img_prefix,
self.id2name[img_id])
db_rec['mask'] = mask
db_rec['joints'] = joints
db_rec['im_shape'] = orgsize
return db_rec
def _get_joints(self, anno, idx):
"""Get joints for all people in an image."""
num_people = len(anno)
joints = np.zeros(
(num_people, self.ann_info['num_joints'], 3), dtype=np.float32)
for i, obj in enumerate(anno):
joints[i, :self.ann_info['num_joints'], :3] = \
np.array(obj['keypoints']).reshape([-1, 3])
img_info = self.coco.loadImgs(self.img_ids[idx])[0]
joints[..., 0] /= img_info['width']
joints[..., 1] /= img_info['height']
orgsize = np.array([img_info['height'], img_info['width']])
return joints, orgsize
def _get_mask(self, anno, idx):
"""Get ignore masks to mask out losses."""
coco = self.coco
img_info = coco.loadImgs(self.img_ids[idx])[0]
m = np.zeros((img_info['height'], img_info['width']), dtype=np.float32)
for obj in anno:
if 'segmentation' in obj:
if obj['iscrowd']:
rle = pycocotools.mask.frPyObjects(obj['segmentation'],
img_info['height'],
img_info['width'])
m += pycocotools.mask.decode(rle)
elif obj['num_keypoints'] == 0:
rles = pycocotools.mask.frPyObjects(obj['segmentation'],
img_info['height'],
img_info['width'])
for rle in rles:
m += pycocotools.mask.decode(rle)
return m < 0.5
@register
@serializable
class KeypointBottomUpCrowdPoseDataset(KeypointBottomUpCocoDataset):
"""CrowdPose dataset for bottom-up pose estimation.
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
CrowdPose keypoint indexes::
0: 'left_shoulder',
1: 'right_shoulder',
2: 'left_elbow',
3: 'right_elbow',
4: 'left_wrist',
5: 'right_wrist',
6: 'left_hip',
7: 'right_hip',
8: 'left_knee',
9: 'right_knee',
10: 'left_ankle',
11: 'right_ankle',
12: 'top_head',
13: 'neck'
Args:
dataset_dir (str): Root path to the dataset.
anno_path (str): Relative path to the annotation file.
image_dir (str): Path to a directory where images are held.
Default: None.
num_joints (int): keypoint numbers
transform (composed(operators)): A sequence of data transforms.
shard (list): [rank, worldsize], the distributed env params
test_mode (bool): Store True when building test or
validation dataset. Default: False.
"""
def __init__(self,
dataset_dir,
image_dir,
anno_path,
num_joints,
transform=[],
shard=[0, 1],
test_mode=False):
super().__init__(dataset_dir, image_dir, anno_path, num_joints,
transform, shard, test_mode)
self.ann_file = os.path.join(dataset_dir, anno_path)
self.shard = shard
self.test_mode = test_mode
def parse_dataset(self):
self.coco = COCO(self.ann_file)
self.img_ids = self.coco.getImgIds()
if not self.test_mode:
self.img_ids = [
img_id for img_id in self.img_ids
if len(self.coco.getAnnIds(
imgIds=img_id, iscrowd=None)) > 0
]
blocknum = int(len(self.img_ids) / self.shard[1])
self.img_ids = self.img_ids[(blocknum * self.shard[0]):(blocknum * (
self.shard[0] + 1))]
self.num_images = len(self.img_ids)
self.id2name, self.name2id = self._get_mapping_id_name(self.coco.imgs)
self.dataset_name = 'crowdpose'
print('=> num_images: {}'.format(self.num_images))
@serializable
class KeypointTopDownBaseDataset(DetDataset):
"""Base class for top_down datasets.
All datasets should subclass it.
All subclasses should overwrite:
Methods:`_get_db`
Args:
dataset_dir (str): Root path to the dataset.
image_dir (str): Path to a directory where images are held.
anno_path (str): Relative path to the annotation file.
num_joints (int): keypoint numbers
transform (composed(operators)): A sequence of data transforms.
"""
def __init__(self,
dataset_dir,
image_dir,
anno_path,
num_joints,
transform=[]):
super().__init__(dataset_dir, image_dir, anno_path)
self.image_info = {}
self.ann_info = {}
self.img_prefix = os.path.join(dataset_dir, image_dir)
self.transform = transform
self.ann_info['num_joints'] = num_joints
self.db = []
def __len__(self):
"""Get dataset length."""
return len(self.db)
def _get_db(self):
"""Get a sample"""
raise NotImplementedError
def __getitem__(self, idx):
"""Prepare sample for training given the index."""
records = copy.deepcopy(self.db[idx])
records['image'] = cv2.imread(records['image_file'], cv2.IMREAD_COLOR |
cv2.IMREAD_IGNORE_ORIENTATION)
records['image'] = cv2.cvtColor(records['image'], cv2.COLOR_BGR2RGB)
records['score'] = records['score'] if 'score' in records else 1
records = self.transform(records)
# print('records', records)
return records
@register
@serializable
class KeypointTopDownCocoDataset(KeypointTopDownBaseDataset):
"""COCO dataset for top-down pose estimation.
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
COCO keypoint indexes:
0: 'nose',
1: 'left_eye',
2: 'right_eye',
3: 'left_ear',
4: 'right_ear',
5: 'left_shoulder',
6: 'right_shoulder',
7: 'left_elbow',
8: 'right_elbow',
9: 'left_wrist',
10: 'right_wrist',
11: 'left_hip',
12: 'right_hip',
13: 'left_knee',
14: 'right_knee',
15: 'left_ankle',
16: 'right_ankle'
Args:
dataset_dir (str): Root path to the dataset.
image_dir (str): Path to a directory where images are held.
anno_path (str): Relative path to the annotation file.
num_joints (int): Keypoint numbers
trainsize (list):[w, h] Image target size
transform (composed(operators)): A sequence of data transforms.
bbox_file (str): Path to a detection bbox file
Default: None.
use_gt_bbox (bool): Whether to use ground truth bbox
Default: True.
pixel_std (int): The pixel std of the scale
Default: 200.
image_thre (float): The threshold to filter the detection box
Default: 0.0.
"""
def __init__(self,
dataset_dir,
image_dir,
anno_path,
num_joints,
trainsize,
transform=[],
bbox_file=None,
use_gt_bbox=True,
pixel_std=200,
image_thre=0.0):
super().__init__(dataset_dir, image_dir, anno_path, num_joints,
transform)
self.bbox_file = bbox_file
self.use_gt_bbox = use_gt_bbox
self.trainsize = trainsize
self.pixel_std = pixel_std
self.image_thre = image_thre
self.dataset_name = 'coco'
def parse_dataset(self):
if self.use_gt_bbox:
self.db = self._load_coco_keypoint_annotations()
else:
self.db = self._load_coco_person_detection_results()
def _load_coco_keypoint_annotations(self):
coco = COCO(self.get_anno())
img_ids = coco.getImgIds()
gt_db = []
for index in img_ids:
im_ann = coco.loadImgs(index)[0]
width = im_ann['width']
height = im_ann['height']
file_name = im_ann['file_name']
im_id = int(im_ann["id"])
annIds = coco.getAnnIds(imgIds=index, iscrowd=False)
objs = coco.loadAnns(annIds)
valid_objs = []
for obj in objs:
x, y, w, h = obj['bbox']
x1 = np.max((0, x))
y1 = np.max((0, y))
x2 = np.min((width - 1, x1 + np.max((0, w - 1))))
y2 = np.min((height - 1, y1 + np.max((0, h - 1))))
if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2 - x1, y2 - y1]
valid_objs.append(obj)
objs = valid_objs
rec = []
for obj in objs:
if max(obj['keypoints']) == 0:
continue
joints = np.zeros(
(self.ann_info['num_joints'], 3), dtype=np.float)
joints_vis = np.zeros(
(self.ann_info['num_joints'], 3), dtype=np.float)
for ipt in range(self.ann_info['num_joints']):
joints[ipt, 0] = obj['keypoints'][ipt * 3 + 0]
joints[ipt, 1] = obj['keypoints'][ipt * 3 + 1]
joints[ipt, 2] = 0
t_vis = obj['keypoints'][ipt * 3 + 2]
if t_vis > 1:
t_vis = 1
joints_vis[ipt, 0] = t_vis
joints_vis[ipt, 1] = t_vis
joints_vis[ipt, 2] = 0
center, scale = self._box2cs(obj['clean_bbox'][:4])
rec.append({
'image_file': os.path.join(self.img_prefix, file_name),
'center': center,
'scale': scale,
'joints': joints,
'joints_vis': joints_vis,
'im_id': im_id,
})
gt_db.extend(rec)
return gt_db
def _box2cs(self, box):
x, y, w, h = box[:4]
center = np.zeros((2), dtype=np.float32)
center[0] = x + w * 0.5
center[1] = y + h * 0.5
aspect_ratio = self.trainsize[0] * 1.0 / self.trainsize[1]
if w > aspect_ratio * h:
h = w * 1.0 / aspect_ratio
elif w < aspect_ratio * h:
w = h * aspect_ratio
scale = np.array(
[w * 1.0 / self.pixel_std, h * 1.0 / self.pixel_std],
dtype=np.float32)
if center[0] != -1:
scale = scale * 1.25
return center, scale
def _load_coco_person_detection_results(self):
all_boxes = None
bbox_file_path = os.path.join(self.dataset_dir, self.bbox_file)
with open(bbox_file_path, 'r') as f:
all_boxes = json.load(f)
if not all_boxes:
print('=> Load %s fail!' % bbox_file_path)
return None
kpt_db = []
for n_img in range(0, len(all_boxes)):
det_res = all_boxes[n_img]
if det_res['category_id'] != 1:
continue
file_name = det_res[
'filename'] if 'filename' in det_res else '%012d.jpg' % det_res[
'image_id']
img_name = os.path.join(self.img_prefix, file_name)
box = det_res['bbox']
score = det_res['score']
im_id = int(det_res['image_id'])
if score < self.image_thre:
continue
center, scale = self._box2cs(box)
joints = np.zeros((self.ann_info['num_joints'], 3), dtype=np.float)
joints_vis = np.ones(
(self.ann_info['num_joints'], 3), dtype=np.float)
kpt_db.append({
'image_file': img_name,
'im_id': im_id,
'center': center,
'scale': scale,
'score': score,
'joints': joints,
'joints_vis': joints_vis,
})
return kpt_db
@register
@serializable
class KeypointTopDownMPIIDataset(KeypointTopDownBaseDataset):
"""MPII dataset for topdown pose estimation.
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
MPII keypoint indexes::
0: 'right_ankle',
1: 'right_knee',
2: 'right_hip',
3: 'left_hip',
4: 'left_knee',
5: 'left_ankle',
6: 'pelvis',
7: 'thorax',
8: 'upper_neck',
9: 'head_top',
10: 'right_wrist',
11: 'right_elbow',
12: 'right_shoulder',
13: 'left_shoulder',
14: 'left_elbow',
15: 'left_wrist',
Args:
dataset_dir (str): Root path to the dataset.
image_dir (str): Path to a directory where images are held.
anno_path (str): Relative path to the annotation file.
num_joints (int): Keypoint numbers
trainsize (list):[w, h] Image target size
transform (composed(operators)): A sequence of data transforms.
"""
def __init__(self,
dataset_dir,
image_dir,
anno_path,
num_joints,
transform=[]):
super().__init__(dataset_dir, image_dir, anno_path, num_joints,
transform)
self.dataset_name = 'mpii'
def parse_dataset(self):
with open(self.get_anno()) as anno_file:
anno = json.load(anno_file)
gt_db = []
for a in anno:
image_name = a['image']
im_id = a['image_id'] if 'image_id' in a else int(
os.path.splitext(image_name)[0])
c = np.array(a['center'], dtype=np.float)
s = np.array([a['scale'], a['scale']], dtype=np.float)
# Adjust center/scale slightly to avoid cropping limbs
if c[0] != -1:
c[1] = c[1] + 15 * s[1]
s = s * 1.25
c = c - 1
joints = np.zeros((self.ann_info['num_joints'], 3), dtype=np.float)
joints_vis = np.zeros(
(self.ann_info['num_joints'], 3), dtype=np.float)
if 'joints' in a:
joints_ = np.array(a['joints'])
joints_[:, 0:2] = joints_[:, 0:2] - 1
joints_vis_ = np.array(a['joints_vis'])
assert len(joints_) == self.ann_info[
'num_joints'], 'joint num diff: {} vs {}'.format(
len(joints_), self.ann_info['num_joints'])
joints[:, 0:2] = joints_[:, 0:2]
joints_vis[:, 0] = joints_vis_[:]
joints_vis[:, 1] = joints_vis_[:]
gt_db.append({
'image_file': os.path.join(self.img_prefix, image_name),
'im_id': im_id,
'center': c,
'scale': s,
'joints': joints,
'joints_vis': joints_vis
})
print("number length: {}".format(len(gt_db)))
self.db = gt_db